An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

Active Banks in the recent economic environment are obliged to encounter in a massive gamut of risks which are closely following them. If cash is regarded as cash at hand, then Liquidity risk is a kind of loss which arises of lack of fund or more specifically endured loss originating from inability of funding required capital in a reasonable way or selling off assets or being forced to have them pledged in order to cover solicited or unsolicited commitments. Hence Liquidity risk is comprised of economic loss incurred due to of providing cash and is deemed vital for operational activities of enterprises. Liquidity Mismatch in banks or maturity mismatch of sensitive assets to cash or debt may culminate in divergent of cash inflow or outflow during elapse of time which is actually stressed as Liquidity risk. Quarterly performance of 23 quoted banks in either Tehran Stock Exchange or Iran Farabourse are executed to model forecasted Banks’ Liquidity risk by means of implementing Artificial Neural Network algorithms. Applying genetic algorithm and Levenberg algorithm helped utilizing the best Training method and subsequently by facilitating Principal Component Analysis (PCA) method, we managed to optimize independent variables. Finally having hidden layers been determined and exercising calculations by Bayesian network model, the Artificial neural network is modeled and tested. All the mentioned process is performed by MATLAB software. Eventually fulfilling the asserted stages, a robust model for anticipating listed banks’ Liquidity risk is developed and findings of models for forcasted data is elaborated.